Orbital Runtime / Adaptive Runtime

Inference That Bends,
Not Breaks

Standard ML runtimes fail when energy is constrained or thermal limits are reached. Adaptive Runtime dynamically adjusts to deliver results within available resources.

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Research Benchmarks

Q2 2026

Production Runtime

2027

See It Adapt

Drag the power slider to simulate eclipse conditions and watch the runtime adapt in real-time.

Power Budget Simulator

Solar Peak

Eclipse (200W) Battery (400W) Solar Peak (900W)

900W

Available Power

FP16

Precision

80/80

Active Layers

8192

Context Tokens

Batch Size

156ms

Latency

0.98

Quality Score

847

Tokens/sec

The Problem

PyTorch and TensorFlow assume unlimited power and cooling. In orbit, energy is variable and thermal rejection is constrained. When a solar eclipse begins, standard runtimes fail.

        The Failure Mode
      

# Standard runtime during eclipse
result = model.generate(prompt)
# ERROR: Power draw exceeds available (847W requested, 340W available)
# ERROR: Thermal limit exceeded (GPU temp 94°C, limit 85°C)
# RESULT: Timeout, request failed

# With Adaptive Runtime
result = adaptive_runtime.generate(prompt, energy_budget=340)
# Automatically adapts: FP16→INT8, skip layers, reduce context
# RESULT: Response delivered in 187ms at 312W
      

Adaptation Strategies

Precision Scaling

Dynamically reduce precision from FP16 → INT8 → INT4 based on power constraints. Trade accuracy for energy efficiency in real-time.

Dynamic Layer Skipping

Identify and skip non-critical layers when thermal headroom is limited. Attention layers are preserved; feed-forward layers are candidates for skipping.

Context Window Reduction

Automatically reduce context window during energy troughs. 8K → 4K → 2K tokens based on available power budget.

Thermal-Aware Batching

Adjust batch size based on radiator capacity and current thermal state. Prevent thermal runaway while maximizing throughput.

Graceful Degradation

Return approximate results rather than timeout. QoS tiers: exact, approximate, best-effort.

Energy Budget as Constraint

Energy budget is a first-class scheduling constraint. The runtime guarantees to stay within budget while maximizing quality.

API Example

          Python - Adaptive Inference
        

from rotastellar import AdaptiveRuntime, QoS

runtime = AdaptiveRuntime(api_key="...")

result = runtime.generate(
    model="llama-70b",
    prompt="Explain orbital mechanics...",
    energy_budget=340,     # Watts
    latency_sla=200,       # ms
    thermal_limit=75,      # °C
    quality=QoS.BEST_EFFORT
)

print(f"Response: {result.text}")
print(f"Actual power: {result.power_w}W")
print(f"Latency: {result.latency_ms}ms")
print(f"Adaptations: {result.adaptations}")
        

          Adaptation Report
        

{
  "adaptations": [
    {
      "type": "precision_reduction",
      "from": "FP16",
      "to": "INT8",
      "power_saved_w": 210
    },
    {
      "type": "layer_skip",
      "layers": [40, 41, 42, 43, 44, 45],
      "quality_impact": "minimal"
    },
    {
      "type": "context_reduction",
      "from_tokens": 8192,
      "to_tokens": 4096
    }
  ],
  "final_power_w": 312,
  "final_latency_ms": 187,
  "quality_score": 0.94
}
        

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Inference That Bends,Not Breaks